Brian Joseph Mcgiverin

US Patent:

7774291, Aug 10, 2010

Filed:

Dec 5, 2008

Appl. No.:

12/329239

Inventors:

James S. Fleming - Apex NC, US
Brian J. McGiverin - Richmond VA, US
Manuel Aparicio, IV - Chapel Hill NC, US

Assignee:

Saffron Technology, Inc. - Morrisville NC

International Classification:

G06F 12/00
G06N 5/02

US Classification:

706 46, 711108, 711E12001

Abstract:

Associative memory systems, methods and/or computer program products include a network of networks of associative memory networks. A network of entity associative memory networks is provided, a respective entity associative memory of which includes associations among a respective observer entity and observed entities that are observed by the respective observer entity, based on input documents. A network of feedback associative memory networks includes associations among observed entities for a respective positive and/or negative evaluation for a respective task of a respective user. A network of document associative memory networks includes associations among observed entities in a respective observed input source, such as a respective input document. A network of community associative memory networks includes associations among a respective observer entity, observed entities that are observed by the respective observer entity, and observed tasks of users in which the observer entity was queried. Associations may be observed into and imagined from the network of networks of associative memory networks.

US Patent:

20060095653, May 4, 2006

Filed:

Nov 3, 2004

Appl. No.:

10/980520

Inventors:

James Fleming - Apex NC, US
Brian McGiverin - Richmond VA, US
Manuel Aparicio - Chapel Hill NC, US

International Classification:

G06F 12/00

US Classification:

711108000

Abstract:

Associative memory systems, methods and/or computer program products include a network of networks of associative memory networks. A network of entity associative memory networks is provided, a respective entity associative memory of which includes associations among a respective observer entity and observed entities that are observed by the respective observer entity, based on input documents. A network of feedback associative memory networks includes associations among observed entities for a respective positive and/or negative evaluation for a respective task of a respective user. A network of document associative memory networks includes associations among observed entities in a respective observed input source, such as a respective input document. A network of community associative memory networks includes associations among a respective observer entity, observed entities that are observed by the respective observer entity, and observed tasks of users in which the observer entity was queried. Associations may be observed into and imagined from the network of networks of associative memory networks.

US Patent:

20080177683, Jul 24, 2008

Filed:

Jan 23, 2007

Appl. No.:

11/626125

Inventors:

Brian Y. No - Kent WA, US
John G. Asay - Olympia WA, US
Brian McGiverin - Newsoms VA, US
John Haager - Bonney Lake WA, US

International Classification:

G06F 15/18
G06N 5/00
G06N 7/02

US Classification:

706 15, 706 45, 706 52

Abstract:

A computer implemented method, apparatus, and computer usable program code for processing sensor data. A network data processing system contains a server, and a plurality of mobile clients. Each mobile client executes on a node using an artificial intelligence process and a memory model located at the node. Each mobile client also receives data from a set of sensors associated with the node to form sensor data, processes the sensor data using the artificial intelligence process and the memory model, and identifies an action based on processing the sensor data. Each mobile client also selectively sends results from processing the sensor data to the server in response to encountering a selected event at the node and receives an update to the memory model based on results from another mobile client. The update provides an ability to process sensor data with respect to new events.